Consistency of Self-Reflection Gains Across Domains

Determine whether multi-round large language model self-reflection—implemented as sequential follow-up calls that prompt the model to critique and revise its previous answer—consistently yields performance improvements across application domains with ambiguous objectives and weak feedback signals, such as machine translation and sentiment classification.

Background

The paper contrasts domains with strong, verifiable feedback (e.g., programming and math, where code execution or symbolic checks provide concrete signals) against production applications like translation and classification, which have more ambiguous objectives and weaker feedback.

While prior work shows self-reflection can help in structured tasks, the generality of these gains across less structured, real-world tasks is uncertain. Establishing whether increases in inference-time computation via self-reflection consistently improve performance across diverse domains is important for guiding deployment decisions under cost and latency constraints.